Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model

Stafoggia, Massimo; Bellander, Tom; Bucci, Simone; Davoli, Marina; Hoogh, Kees de; Donato, Francesca De'; Gariazzo, Claudio; Lyapustin, Alexei; Michelozzi, Paola; Renzi, Matteo; Scortichini, Matteo; Shtein, Alexandra; Viegi, Giovanni; Kloog, Itai; Schwartz, Joel

doi:10.1016/j.envint.2019.01.016

Cited by 297 publications

(112 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We therefore trained a four-stage model to predict daily PM 10 (2006-2015) and PM 2.5 (2011-2015) concentrations for each 1 × 1 km (according to the International System of Units, SI) grid cell: The accuracy of the measurements was evaluated through a 10-fold cross-validation approach, by estimating the R 2 , i.e., the percentage of variability of the observed PM values captured by the predictions. Cross-validated R 2 were 0.71 and 0.75 for PM 10 and PM 2.5 , respectively, demonstrating fair predictive properties [12].…”

Section: Particulate Matter (Pm) Exposure Levelsmentioning

confidence: 90%

“…For each individual residential address, annual mean concentrations of PM 10 and PM 2.5 for the year 2011 were estimated through a Random Forest Machine Learning Approach (RFMLA); the entire process is fully described elsewhere [12,15,16]. Briefly, for each day of the years between 2006 and 2015, and for each squared kilometer of Italy, we collected several spatial and spatiotemporal parameters.…”

Section: Particulate Matter (Pm) Exposure Levelsmentioning

confidence: 99%

“…There is evidence that air pollution can exacerbate existing respiratory health problems [1,9]; however, whether air pollution may also cause respiratory disease is less certain, since previous studies show conflicting results [10] with limitations on exposure assessment and on the ability to control for individual potential confounders [11]. Recently, the quality of the exposure assessment has been improved, using exposure models that combine data from air quality monitoring stations and satellite data, along with territorial data (e.g., land use) and meteorological parameters [12,13]. These new techniques allow investigating long-term health effects through individual and objective measures of air pollutants exposure.…”

Section: Introductionmentioning

confidence: 99%

“…In this framework, the "Big data in Environmental and occupational EPidemiology" (BEEP) project, co-funded by the Italian Workers' Compensation Authority (INAIL), was designed. The aim was to investigate the health effects of air pollution, noise and meteorological parameters on the Italian general population through integration of national data including land use, satellite, modelled meteorological fields and atmospheric composition variables, mortality records, hospitalizations, morbidity, work injuries and commuting accidents [12,14,15]. Within BEEP, a spatiotemporal machine learning model, the "random forest", was developed to estimate daily mean PM 10 (PM with aerodynamic diameter ≤10 microns) and PM 2.5 (PM with aerodynamic diameter ≤2.5 microns) concentrations for each squared kilometer of Italy in the period 2006-2015 [12,16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effects of Particulate Matter on the Incidence of Respiratory Diseases in the Pisan Longitudinal Study

Fasola

Maio

Baldacci

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

The current study aimed at assessing the effects of exposure to Particulate Matter (PM) on the incidence of respiratory diseases in a sub-sample of participants in the longitudinal analytical epidemiological study in Pisa, Italy. Three hundred and five subjects living at the same address from 1991 to 2011 were included. Individual risk factors recorded during the 1991 survey were considered, and new cases of respiratory diseases were ascertained until 2011. Average PM 10 and PM 2.5 exposures (µg/m 3 , year 2011) were estimated at the residential address (1-km 2 resolution) through a random forest machine learning approach, using a combination of satellite data and land use variables. Multivariable logistic regression with Firth's correction was applied. The median (25th-75th percentile) exposure levels were 30.1 µg/m 3 (29.9-30.7 µg/m 3 ) for PM 10 and 19.3 µg/m 3 (18.9-19.4 µg/m 3 ) for PM 2.5 . Incidences of rhinitis and chronic phlegm were associated with increasing PM 2.5 : OR = 2.25 (95% CI: 1.07, 4.98) per unit increase (p.u.i.) and OR = 4.17 (1.12, 18.71) p.u.i., respectively. Incidence of chronic obstructive pulmonary disease was associated with PM 10 : OR = 2.96 (1.50, 7.15) p.u.i. These results provide new insights into the long-term respiratory health effects of PM air pollution.

show abstract

Section: Particulate Matter (Pm) Exposure Levelsmentioning

confidence: 90%

Section: Particulate Matter (Pm) Exposure Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of Particulate Matter on the Incidence of Respiratory Diseases in the Pisan Longitudinal Study

Fasola

Maio

Baldacci

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…More in particular, average values as high as 50 µg/m 3 under more stable atmospheric conditions were reported, with concentrations below 20 µg/m 3 during the warmest months. [21][22][23][24].…”

Section: Discussionmentioning

confidence: 99%

Comparison Study between Indoor and Outdoor Chemical Composition of PM2.5 in Two Italian Areas

2020

View full text Add to dashboard Cite

Outdoor air quality guidelines have been constantly implemented during the last decades. Nonetheless, no international regulations have been put into action in terms of indoor air quality standards and standardized procedures for indoor pollution measurements. In this study, we investigated the chemical composition of PM 2.5 collected outdoors and indoors at six dwellings located in two Italian areas. The selected sites concerned inland/central and southern Italy, including urban, peri-urban, rural and coastal settings. The seasonal and site-specific particulate matter (PM) variations were analyzed outdoors and indoors, by estimating the impact of the main macro-sources and the contribution of the macro-and micro-components. Outdoors, organic matter represented the main contribution at inland and coastal sites, respectively during winter and summer. A clear, seasonal variation was also observed for secondary inorganic species. A site-specific dependence was exhibited by traffic-related components. Indoors, organic and soil-related species were influenced by the presence of the inhabitants. Some specific tracers allowed to identify additional local source contributions and indoor activities. Although the sampling season and site location defined the outdoor air quality, the higher PM concentrations and the chemical composition indoors were influenced by the infiltration of outdoor air and by the indoor activities carried out by its inhabitants.Atmosphere 2020, 11, 368 2 of 27 discussion of the scientific evidences about the adverse health effects of air pollution. This technical paper highlights that several components contribute to the health effects of PM, but that there is not enough evidence to associate the health outcomes to specific emission sources. The report underlines that most of the evidences are accumulated on cardiovascular and respiratory outcomes, that are generally related to traffic emissions, coal and oil combustion and biomass combustion, particularly in indoor environments located in low-income countries [5].Although scientific literature demonstrated that people spend more than 90% of their time in indoor environments, notably in dwellings, schools, offices and means of transport, no supranational legislation has been implemented in order to encompass indoor air quality standards [6,7]. In addition, no standard procedures have been introduced yet, in order to monitor indoor contaminants.Indeed, the evaluation of the exposure to indoor pollutants is far from being a simple task. The concentration and chemical composition of indoor air, in fact, depends on the release of contaminants from indoor sources as well as on their penetration from outdoors. As far as PM is concerned, the few studies whose focus was set on the chemical properties of particles suspended in indoor environments have required remarkable efforts. For this reason, they mostly concerned specific group of hazardous components such as elements, carbonaceous compounds or classes of species like polycyclic aromatic hydrocarbons [...

show abstract

Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries

Ma,

Nobile,

Marb

et al. 2024

JAMA Netw Open

Self Cite

View full text Add to dashboard Cite

ImportanceThe association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space.ObjectiveTo estimate the association between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates using a causal modeling approach.Design, Setting, and ParticipantsThis cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023.ExposureDay-to-day changes in county- or municipality-level mean PM2.5 and NO2 concentrations.Main Outcomes and MeasuresDay-to-day changes in county- or municipality-level all-cause mortality rates.ResultsAmong the 8 963 352 deaths in the 4 study regions, a 10-μg/m3 increase in daily PM2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-μg/m3 increase in NO2 concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO2).Conclusions and RelevanceThe findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.

show abstract

Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model

Cited by 297 publications

References 40 publications

Effects of Particulate Matter on the Incidence of Respiratory Diseases in the Pisan Longitudinal Study

Effects of Particulate Matter on the Incidence of Respiratory Diseases in the Pisan Longitudinal Study

Comparison Study between Indoor and Outdoor Chemical Composition of PM2.5 in Two Italian Areas

Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries

Contact Info

Product

Resources

About